@inproceedings{zhou-etal-2018-ecnu,
    title = "{ECNU} at {S}em{E}val-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection",
    author = "Zhou, Yunxiao  and
      Lan, Man  and
      Wu, Yuanbin",
    editor = "Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      May, Jonathan  and
      Shutova, Ekaterina  and
      Bethard, Steven  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/S18-1165/",
    doi = "10.18653/v1/S18-1165",
    pages = "999--1002",
    abstract = "This paper describes the system we submitted to Task 10 (Capturing Discriminative Attributes) in SemEval 2018. Given a triple (word1, word2, attribute), this task is to predict whether it exemplifies a semantic difference or not. We design and investigate several word embedding features, PMI features and WordNet features together with supervised machine learning methods to address this task. Officially released results show that our system ranks above average."
}